Analytics query accelerators provide SQL or SQL-like query support on a broad range of data sources. They are most frequently used as a means of providing interactive and production-optimized delivery on semantically flexible data stores that do not inherently have the capabilities to provide sufficient performance or ease of use on their own. Commonly used in conjunction with data lakes, they aim to support BI dashboards, interactive query capabilities, data modeling and other analytics use cases.
Gartner defines the cloud database management system (DBMS) market as being that for products from vendors that supply fully provider-managed public or private cloud software systems that manage data in cloud storage. Data is stored in a cloud storage tier (such as a cloud object store, a distributed data store or other proprietary cloud storage infrastructure), and may use multiple data models — relational, nonrelational (document, key-value, wide-column, graph), geospatial, time series and others. These DBMSs reflect optimization strategies designed to support transactions and/or analytical processing for one or more of the following use cases: Traditional and augmented transaction processing, Traditional and logical data warehouse, Data science exploration/deep learning, Stream/event processing, and Operational intelligence This market does not include vendors that only provide DBMSs hosted in infrastructure as a service (IaaS), such as in a virtual machine or container, and managed by the customer.
This market evaluates vendors of data science and machine-learning platforms. These are software products that data scientists use to help them develop and deploy their own data science and machine-learning solutions. More precisely, Gartner defines a data science and machine-learning platform as: A cohesive software application that offers a mixture of basic building blocks essential both for creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products. Machine learning is a popular subset of data science that warrants specific attention when evaluating these platforms.